NCT07327970

Prospective Validation of Ataraxis AI Test for Predicting Treatment Response in Neoadjuvant Breast Cancer

A Prospective Non-Interventional Study Using a Multi-Modal Prognostic Test (Ataraxis) for Evaluating the Clinical Integration in Early-Stage Invasive Breast Cancer

• Status: Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 150 (estimated)
• Sponsor: Young-Joon Kang · Academic / Other
• Sex: Female
• Age: 18 Years
• Healthy volunteers: Not accepted

Summary

This study evaluates the real-world clinical workflow integration of a previously developed artificial intelligence (AI) prognostic test in breast cancer patients receiving neoadjuvant chemotherapy, and validates its accuracy in predicting treatment response. The Ataraxis AI test analyzes digitized images of tumor biopsy slides combined with basic clinical information (age, tumor stage, hormone receptor status) to generate a risk score. Prior studies showed the AI test can predict cancer recurrence with accuracy comparable to or better than existing genomic tests. The study has two stages: * Stage 1 (30 patients): Assess whether the AI test can be practically integrated into routine clinical workflow, including ease of use, report clarity, and time requirements. * Stage 2 (70-120 additional patients): Validate the accuracy of AI-predicted pathological complete response (pCR) rates against actual surgical outcomes. This study uses a blinded design where treating physicians remain blinded to AI results until post-surgical pCR assessment. AI analysis is performed by the research coordinator in collaboration with Ataraxis. After pCR evaluation, AI results are disclosed and physicians complete surveys assessing hypothetical treatment changes. This design eliminates AI influence on treatment decisions and ensures independent validation. Participants are adults with Stage I-III breast cancer planned for neoadjuvant chemotherapy. The study involves no additional procedures beyond standard care except for completing surveys about the AI test experience.

Conditions

• Breast Cancer

Interventions

Type	Name	Description
DIAGNOSTIC_TEST	multi-modal foundation AI test	Multi-modal AI test combining digital pathology features from H\&E-stained core needle biopsy slides with clinical information (age, molecular biomarkers, TNM stage) to generate a continuous risk score (0-1) predicting pathological complete response. Results provided as reference information only; does not influence treatment decisions.

Timeline

• Start date: 2026-01-20
• Primary completion: 2027-08-31
• Completion: 2027-12-31
• First posted: 2026-01-08
• Last updated: 2026-03-27

Locations

1 site across 1 country: South Korea